"""simple docstring"""



from ...processing_utils import ProcessorMixin






class  snake_case__      (       snake_case_       ):
 _snake_case       :					List[str]							  =						"""SpeechT5FeatureExtractor"""
 _snake_case       :					int							  =						"""SpeechT5Tokenizer"""


 def __init__( self    ,		lowerCamelCase    ,		lowerCamelCase						):
        super().__init__(lowerCamelCase    ,		lowerCamelCase						)


 def __call__( self    ,		*lowerCamelCase    ,		**lowerCamelCase						):
        __a       =    kwargs.pop("audio"    ,		lowerCamelCase						)
        __a       =    kwargs.pop("text"    ,		lowerCamelCase						)
        __a       =    kwargs.pop("text_target"    ,		lowerCamelCase						)
        __a       =    kwargs.pop("audio_target"    ,		lowerCamelCase						)
        __a       =    kwargs.pop("sampling_rate"    ,		lowerCamelCase						)

        if audio is not None and text is not None:
               raise ValueError(
                   "Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"						)
        if audio_target is not None and text_target is not None:
               raise ValueError(
                   "Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"						)
        if audio is None and audio_target is None and text is None and text_target is None:
               raise ValueError(
                   "You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."						)

        if audio is not None:
               __a       =    self.feature_extractor(lowerCamelCase    ,		*lowerCamelCase    ,		sampling_rate=lowerCamelCase    ,		**lowerCamelCase						)
        elif text is not None:
               __a       =    self.tokenizer(lowerCamelCase    ,		**lowerCamelCase						)
        else:
               __a       =    None

        if audio_target is not None:
               __a       =    self.feature_extractor(audio_target=lowerCamelCase    ,		*lowerCamelCase    ,		sampling_rate=lowerCamelCase    ,		**lowerCamelCase						)
               __a       =    targets["input_values"]
        elif text_target is not None:
               __a       =    self.tokenizer(lowerCamelCase    ,		**lowerCamelCase						)
               __a       =    targets["input_ids"]
        else:
               __a       =    None

        if inputs is None:
               return targets

        if targets is not None:
               __a       =    labels

               __a       =    targets.get("attention_mask"						)
               if decoder_attention_mask is not None:
                      __a       =    decoder_attention_mask

        return inputs


 def 						a__   ( self    ,		*lowerCamelCase    ,		**lowerCamelCase						):
        __a       =    kwargs.pop("input_values"    ,		lowerCamelCase						)
        __a       =    kwargs.pop("input_ids"    ,		lowerCamelCase						)
        __a       =    kwargs.pop("labels"    ,		lowerCamelCase						)

        if input_values is not None and input_ids is not None:
               raise ValueError("Cannot process both `input_values` and `input_ids` inputs."						)
        if input_values is None and input_ids is None and labels is None:
               raise ValueError(
                   "You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."						)

        if input_values is not None:
               __a       =    self.feature_extractor.pad(lowerCamelCase    ,		*lowerCamelCase    ,		**lowerCamelCase						)
        elif input_ids is not None:
               __a       =    self.tokenizer.pad(lowerCamelCase    ,		**lowerCamelCase						)
        else:
               __a       =    None

        if labels is not None:
               if "input_ids" in labels or (isinstance(lowerCamelCase    ,		lowerCamelCase						) and "input_ids" in labels[0]):
                      __a       =    self.tokenizer.pad(lowerCamelCase    ,		**lowerCamelCase						)
                      __a       =    targets["input_ids"]
               else:
                      __a       =    self.feature_extractor.feature_size
                      __a       =    self.feature_extractor.num_mel_bins
                      __a       =    self.feature_extractor.pad(lowerCamelCase    ,		*lowerCamelCase    ,		**lowerCamelCase						)
                      __a       =    feature_size_hack
                      __a       =    targets["input_values"]
        else:
               __a       =    None

        if inputs is None:
               return targets

        if targets is not None:
               __a       =    labels

               __a       =    targets.get("attention_mask"						)
               if decoder_attention_mask is not None:
                      __a       =    decoder_attention_mask

        return inputs


 def 						a__   ( self    ,		*lowerCamelCase    ,		**lowerCamelCase						):
        return self.tokenizer.batch_decode(*lowerCamelCase    ,		**lowerCamelCase						)






 def 						a__   ( self    ,		*lowerCamelCase    ,		**lowerCamelCase						):
        return self.tokenizer.decode(*lowerCamelCase    ,		**lowerCamelCase						)

"""simple docstring"""



import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


SCREAMING_SNAKE_CASE__:Dict									=			logging.get_logger(__name__)






class  snake_case__      (       snake_case_       ):


 def __init__( self    ,		*lowerCamelCase    ,		**lowerCamelCase						):
        warnings.warn(
            "The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
            " use DonutImageProcessor instead."    ,		lowerCamelCase    ,		)
        super().__init__(*lowerCamelCase    ,		**lowerCamelCase						)
